Access the full text.
Sign up today, get DeepDyve free for 14 days.
Lang & Wootton (JLW) Jones
Property Index
G. Ljung, G. Box (1978)
On a measure of lack of fit in time series modelsBiometrika, 65
William Wheaton, Raymond Torto (1988)
Vacancy Rates and the Future of Office RentsReal Estate Economics, 16
C. Gardiner, J. Henneberry (1991)
Predicting Regional Office Rents Using Habit‐persistence TheoriesJournal of Property Valuation and Investment, 9
K. McClure
Estimating occupied office space: comparing forecast methodologies
C. Gardiner, J. Henneberry
The development of a simple regional model of office rent prediction
G. Box, David Pierce (1970)
Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series ModelsJournal of the American Statistical Association, 65
B. Giussani, S. Tsolacos
The office market in the UK: modelling the determinants of rental values
C. Gardiner, J. Henneberry (1989)
The development of a simple regional office rent prediction model, 7
S. Hylleberg, R. Engle, C. Granger, Byung Yoo (1990)
Seasonal integration and cointegrationJournal of Econometrics, 44
J.T. Hetherington
Forecasting of rents
H. Kelly
Forecasting office space demand in urban areas
P. Shearer, N. Farnum, L. Stanton (1990)
Quantitative Forecasting MethodsThe Statistician, 39
J. Shilling, J. Corgel, Peter Colwell, Jim Barth, John Clapp, Mel Jameson (1987)
Price adjustment process for rental office spaceJournal of Urban Economics, 22
William Wheaton (1987)
The Cyclic Behavior of the National Office MarketReal Estate Economics, 15
John Hekman (1985)
Rental Price Adjustment and Investment in the Office MarketReal Estate Economics, 13
M. Bartlett (1946)
On the Theoretical Specification and Sampling Properties of Autocorrelated Time‐Series, 8
Kenneth Rosen (1984)
Toward a Model of the Office Building SectorReal Estate Economics, 12
P. Ilmakunnas
Testing the order of differencing in quarterly data: an illustration of the testing sequence
The application of short‐term forecasting techniques to the prediction of commercial rental values generates valuable information about the dynamics of rent movements. It also captures short‐run trends more effectively than do other forecasting procedures. Makes use of ARIMA models to provide one‐step‐ahead predictions. The results show that ARIMA models perform better in the case of retail and office sectors. The forecasts for these sectors are satisfactory. Retail rents bear a relationship to their past values, whereas office rents are influenced by shocks in the market – demand or supply driven. The results of the present study are useful for incorporation in more general models of rent forecasting. Also presents a full methodology which facilitates its application.
Journal of Property Valuation and Investment – Emerald Publishing
Published: Dec 1, 1995
Keywords: Commercial property; Industrial property; Property markets; Rental value; Short‐term forecasting; United Kingdom
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.